Generally, the detailed mechanisms by which most genes, cells and viruses function in humans and other organisms are relatively unknown, despite the fact that there have been successful genomic sequencing programs and other extensive functional genomics efforts. Consequently, there is a need for screening approaches that enable high-throughput examination and elucidation of gene functions in a relevant physiological environment.
High-throughput, automated fluorescence microscopy makes it possible to screen and analyze the functions of hundreds of thousands of gene products in the context of the cell. Because microscopy has the potential to yield significantly more sub-cellular information than other detection methods, the term ‘high content’ has been adopted to describe screens and assays that are detected and quantified using automated imaging platforms. Both image acquisition and image analysis can be automated and then optimized to maximize throughput on high-content analysis systems. Two core components of the computational tools required for automated image analysis are the segmentation and positional tracking of individual cells.
Motility and division are two fundamental cell behaviors that are of great importance in a number of areas of research, including oncology, immunology, and developmental biology. While these behaviors have unique analysis requirements, they also share several significant analysis challenges. Examination of these behaviors requires time-lapse imaging Implementation of time-lapse imaging for large-scale experimentation poses challenges with respect to finding assay, imaging, and analysis parameters that will be optimal across the wide variety of treatment conditions and behavioral phenotypes represented in a particular data set. Another challenge is the frequent problem of low contrast-to-noise ratios in acquired images. In live-cell time-lapse experiments, poor contrast-to-noise ratios often arise from deliberate reduction of dye concentrations in order to avoid unwanted toxic side-effects such as DNA-binding, which inhibits DNA replication in living cells. An added challenge is that automated focus mechanisms occasionally fail under the demands of high-speed acquisition protocols. Model systems also typically contain far more targets (cells) than are monitored with common surveillance applications. Specific biological events such as mitosis also need to be handled.
Existing analysis routines for cell tracking can be roughly divided into two main approaches: independent detection with subsequent data association, and model-based tracking. In the publication by Li et al., entitled “Online tracking of migrating and proliferating cells imaged with phase-contrast microscopy”, the aforementioned approaches are combined by both segmenting each frame separately and using a multi-target tracking system using model propagation with level sets and a stochastic motion filter, which is hereby incorporated by reference. Proc. CVPRW. (2006) pages 65-72. In another publication by Padfield et al., entitled “Spatio-temporal Cell Cycle Analysis using 3D level set segmentation of unstained nuclei in line scan confocal images”, another approach is utilized for the tracking task as a spatio-temporal segmentation task, which is hereby incorporated by reference. IEEE ISBI, Arlington, Va. (April 2006).
There is a need for a system and method that detects corruptions in images of biological materials to enable elimination of poor images and provide better segmentation and tracking of objects comprising the biological material.